Neighborhood Variation in the Utilization of Laparoscopy for the Treatment of Colon Cancer
Bibliographic record
Abstract
BACKGROUND: The rates of laparoscopic colectomy for colon cancer have steadily increased since its inception. Laparoscopic colectomy currently accounts for a third of colectomy procedures in the United States, but little is known regarding the spatial pattern of the utilization of laparoscopy for colon cancer. OBJECTIVE: This study evaluated the utilization of laparoscopy for colon cancer at the neighborhood level in Ontario. DESIGN: Retrospective analysis of prospectively collected data was performed. SETTING: This study was conducted at all hospitals in the province of Ontario. PATIENTS: This population-based study included all patients aged ≥18 who received an elective colectomy for colon cancer from April 2008 until March 2012 in the province of Ontario. MAIN OUTCOME MEASURES: The primary outcome measure was the neighborhood rates of laparoscopy. RESULTS: Overall, 9,969 patients underwent surgery, and the cluster analysis identified 74 cold-spot neighborhoods, representing 1.8 million people, or 14% of the population. In the multivariate analysis, patients from rural neighborhoods were less than half as likely to receive laparoscopy, OR 0.44 (95% CI, 0.24-0.84; p = 0.012). Additionally, having a minimally invasive surgery fellowship training facility within the same administrative health region as the neighborhood made it more than 23 times as likely to be a hot spot, OR 25.88 (95% CI, 12.15-55.11; p < 0.001). Neighborhood socioeconomic status was not associated with variation in the utilization of laparoscopy. LIMITATIONS: Patient case mix could affect laparoscopy use. CONCLUSION AND RELEVANCE: This study identified an unequal utilization of laparoscopy for colon cancer within Ontario with rural neighborhoods experiencing low rates of laparoscopic colectomy, whereas neighborhoods in the same administrative region as minimally invasive surgery training centers experienced increased utilization. Further study into the causes of this variation in resource allocation is needed to identify ways to improve more efficient spread of knowledge and technical skills advancement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".